This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of phosphate using anion ion chromatography (IC) or other methods. A unit of SRM 3186 consists of five 10 mL sealed borosilicate glass ampoules of solution prepared gravimetrically to contain a known mass fraction of phosphate dissolved in filtered (0.22 ?m) water having a minimum resistivity of 18 megaohm centimeters. This data is public in the Certificate of Analysis for this material.

Standard Reference Material SRM 915c Calcium Carbonate - This Standard Reference Material (SRM) is intended for use as an analytical standard of known purity. It is intended primarily for use in the calibration and standardization of procedures for calcium (Ca) determinations employed in clinical analysis and for routine critical evaluation of the daily working standards used in these procedures. This data is public in the Certificate of Analysis for this material.

Integrated Path Differential Absorption LIDAR Measurement of CO2, CH4, and H2O near 1.6 microns

This folder contains image data sets from 14 separate serial sectioning sessions. The entire data folder consists of 1379 8 bit tif images and is 47.3 GB in size. Serial sectioning was performed using FEI Helios 660 NanoLab focused ion beam scanning electron microscope (FIB SEM) and Auto Slice and View G3 software. The sample was a heavy metal stained and resin embedded Caenorhabditis elegans (C. elegans) that were exposed to 60 nm Au nanoparticles. Detailed descriptions of the worm preparation and resin block processing are described in Johnson, M.E. et al. (ACS Nano, 2016).

Standard Reference Material-1721: Southern Oceanic Air. Ambient Nominal Amount of Substance Fraction for Carbon Dioxide, Methane & Nitrous Oxide. This Standard Reference Material (SRM) is a primary gas mixture for which the amount-of-substance fraction, expressed as concentration, may be related to secondary working standards. This SRM is intended for the calibration of instruments used for ambient carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) determinations and for other applications. This data is public in the Certificate of Analysis for this material.

We have applied 3D shape-based retrieval to various disciplines such as computer vision, CAD/CAM, computer graphics, molecular biology and 3D anthropometry. We have organized two workshops on 3D shape retrieval and two shape retrieval contests. We also have developed 3D shape benchmarks, performance evaluation software and prototype 3D retrieval systems. We have developed a robotic map quality assessment tool in collaboration with MEL) We also have developed different shape descriptors to represent 3D human bodies and heads efficiently and other work related to 3D anthropometry.

The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.

Task description:
In response to a given set of range scan queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query.

The objective of SHREC'14 Track is to evaluate the performances of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a generic 3D model dataset.

Objective:
The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-sale comprehensive 3D shape database which contains different types of models, such as generic, articulated, CAD and architecture models.

Objective: The objective of this track is to evaluate the performance of 3D shape retrieval approaches on Generic 3D Dataset.

Introduction: With the increasing number of 3D models are created every day and stored in databases, effectively searching a 3D repository for 3D shapes which are similar to a given 3D query model has become an important area of research. Benchmarking allows researchers to evaluate the quality of the results of different 3D shape retrieval approaches.